Analyze selection data using soluble Ephrin-B2 or -B3¶
In [1]:
# this cell is tagged as parameters for `papermill` parameterization
#input configs
altair_config = None
nipah_config = None
#input files
entropy_file = None
func_scores_E2_file = None
binding_E2_file = None
func_scores_E3_file = None
binding_E3_file = None
#output files
filtered_E2_binding_data = None
filtered_E3_binding_data = None
filtered_E2_binding_low_effect = None
filtered_E3_binding_low_effect = None
#output images
entry_binding_combined_corr_plot = None
entry_binding_combined_corr_plot_agg = None
E2_E3_correlation = None
E2_E3_correlation_site = None
combined_E2_E3_site_corr = None
binding_by_site_plot = None
entry_binding_corr_heatmap = None
binding_corr_heatmap = None
binding_region_boxplot_plot = None
binding_region_bubble_plot = None
max_binding_in_stalk = None
max_binding_in_contact = None
In [2]:
# Parameters
nipah_config = "nipah_config.yaml"
altair_config = "data/custom_analyses_data/theme.py"
entropy_file = "results/entropy/entropy.csv"
func_scores_E2_file = "results/func_effects/averages/CHO_EFNB2_low_func_effects.csv"
binding_E2_file = "results/receptor_affinity/averages/EFNB2_monomeric_mut_effect.csv"
func_scores_E3_file = "results/func_effects/averages/CHO_EFNB3_low_func_effects.csv"
binding_E3_file = "results/receptor_affinity/averages/EFNB3_dimeric_mut_effect.csv"
filtered_E2_binding_data = "results/filtered_data/E2_binding_filtered.csv"
filtered_E3_binding_data = "results/filtered_data/E3_binding_filtered.csv"
filtered_E2_binding_low_effect = (
"results/filtered_data/E2_binding_low_effect_filter.csv"
)
filtered_E3_binding_low_effect = (
"results/filtered_data/E3_binding_low_effect_filter.csv"
)
entry_binding_combined_corr_plot = (
"results/images/entry_binding_combined_corr_plot.html"
)
entry_binding_combined_corr_plot_agg = (
"results/images/entry_binding_combined_corr_plot_agg.html"
)
E2_E3_correlation = "results/images/E2_E3_correlation.html"
E2_E3_correlation_site = "results/images/E2_E3_correlation_site.html"
combined_E2_E3_site_corr = "results/images/combined_E2_E3_site_corr.html"
binding_by_site_plot = "results/images/binding_by_site_plot.html"
entry_binding_corr_heatmap = "results/images/entry_binding_corr_heatmap.html"
binding_corr_heatmap = "results/images/binding_corr_heatmap.html"
binding_region_boxplot_plot = "results/images/binding_region_boxplot_plot.html"
binding_region_bubble_plot = "results/images/binding_region_bubble_plot.html"
max_binding_in_contact = "results/images/max_binding_in_contact.html"
max_binding_in_stalk = "results/images/max_binding_in_stalk.html"
In [3]:
import math
import os
import re
import altair as alt
import numpy as np
import pandas as pd
import scipy.stats
import yaml
In [4]:
# allow more rows for Altair
_ = alt.data_transformers.disable_max_rows()
if os.getcwd() == '/fh/fast/bloom_j/computational_notebooks/blarsen/2023/Nipah_Malaysia_RBP_DMS/':
pass
print("Already in correct directory")
else:
os.chdir("/fh/fast/bloom_j/computational_notebooks/blarsen/2023/Nipah_Malaysia_RBP_DMS/")
print("Setup in correct directory")
Setup in correct directory
In [5]:
if nipah_config is None:
##hard paths in case don't want to run with snakemake
print('loading hard paths')
altair_config = "data/custom_analyses_data/theme.py"
nipah_config = "nipah_config.yaml"
entropy_file = 'results/entropy/entropy.csv'
#input files
func_scores_E2_file = "results/func_effects/averages/CHO_EFNB2_low_func_effects.csv"
binding_E2_file = "results/receptor_affinity/averages/EFNB2_monomeric_mut_effect.csv"
func_scores_E3_file = "results/func_effects/averages/CHO_EFNB3_low_func_effects.csv"
binding_E3_file = "results/receptor_affinity/averages/EFNB3_dimeric_mut_effect.csv"
filtered_E2_binding_data="results/filtered_data/E2_binding_filtered.csv"
filtered_E3_binding_data="results/filtered_data/E3_binding_filtered.csv"
filtered_E2_binding_low_effect="results/filtered_data/E2_binding_low_effect_filter.csv"
filtered_E3_binding_low_effect="results/filtered_data/E3_binding_low_effect_filter.csv"
Run config files to setup altair theme and config variables¶
In [6]:
if altair_config:
with open(altair_config, 'r') as file:
exec(file.read())
with open(nipah_config) as f:
config = yaml.safe_load(f)
Make the E2/E3 dataframes, filter separately, then merge¶
In [7]:
#import binding and entry data
e2 = pd.read_csv(binding_E2_file)
e2_func = pd.read_csv(func_scores_E2_file)
e3 = pd.read_csv(binding_E3_file)
e3_func = pd.read_csv(func_scores_E3_file)
Filter the data and save¶
In [8]:
def merge_func_binding_dfs(func,binding,name):
df_int = pd.merge(
binding,
func,
on=['site','mutant','wildtype'],
suffixes=['_binding','_cell_entry'],
validate='one_to_one',
how='outer'
).round(3)
df = df_int.rename(columns={'Ephrin binding_mean':'binding_mean','Ephrin binding_std':'binding_std','Ephrin binding_median':'binding_median'})
# Only save relevant columns
df = df[['site','wildtype','mutant','binding_median','binding_std','times_seen_binding','effect','effect_std','times_seen_cell_entry','frac_models']]
def filter_binding_data(df):
df_filter = df[
(df['mutant'] != '*') &
(df['mutant'] != '-') &
(df['site'] != 603) &
# Filter cell entry parameters
(df['effect'] >= config['min_func_effect_for_binding']) &
(df['times_seen_cell_entry'] >= config['func_times_seen_cutoff']) &
(df['effect_std'] <= config['func_std_cutoff']) &
# Filter binding parameters
(df['times_seen_binding'] >= config['min_times_seen_binding']) &
(df['binding_std'] <= config['max_binding_std']) &
(df['frac_models'] >= config['frac_models'])
]
return df_filter
df_filter = filter_binding_data(df)
#For pulling out low effect mutants for heatmaps later. Find mutants below func effect cutoff, but still have ok times_seen and func_std.
def store_filtered_info(df):
df_low_filter = df[
(df['mutant'] != '*') &
(df['mutant'] != '-') &
(df['site'] != 603) &
(df['effect'] < config['min_func_effect_for_binding']) &
(df['times_seen_cell_entry'] >= config['func_times_seen_cutoff']) &
(df['effect_std'] <= config['func_std_cutoff'])
]
return df_low_filter
df_low_effect_filter = store_filtered_info(df)
if name == 'EFNB2':
print(name)
df_filter.to_csv(filtered_E2_binding_data,index=False)
df_low_effect_filter.to_csv(filtered_E2_binding_low_effect,index=False)
else:
df_filter.to_csv(filtered_E3_binding_data,index=False)
df_low_effect_filter.to_csv(filtered_E3_binding_low_effect,index=False)
return df_filter,df_low_effect_filter
#Call filtering function
df_E2_filter,df_E2_filter_missing = merge_func_binding_dfs(e2_func,e2,'EFNB2')
df_E3_filter,df_E3_filter_missing = merge_func_binding_dfs(e3_func,e3,'EFNB3')
#Now that they are filtered, merge EFNB2 and EFNB3
df_binding_effect_merge = pd.merge(
df_E2_filter,
df_E3_filter,
on=['site','wildtype','mutant'],
suffixes=['_E2','_E3'],
how='outer'
)
#display stats
display(df_binding_effect_merge.describe().round(3))
# Make a concat df of E2/E3 data for plotting later
df_E2_filter['selection'] = 'EFNB2'
df_E3_filter['selection'] = 'EFNB3'
df_binding_effect_concat = pd.concat([df_E2_filter,df_E3_filter])
EFNB2
| site | binding_median_E2 | binding_std_E2 | times_seen_binding_E2 | effect_E2 | effect_std_E2 | times_seen_cell_entry_E2 | frac_models_E2 | binding_median_E3 | binding_std_E3 | times_seen_binding_E3 | effect_E3 | effect_std_E3 | times_seen_cell_entry_E3 | frac_models_E3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 7117.00 | 6601.000 | 6601.000 | 6601.000 | 6601.000 | 6601.000 | 6601.000 | 6601.000 | 6408.000 | 6408.000 | 6408.000 | 6408.000 | 6408.000 | 6408.000 | 6408.0 |
| mean | 344.09 | -0.327 | 0.496 | 6.009 | -0.063 | 0.369 | 7.409 | 0.997 | -0.017 | 0.177 | 5.863 | -0.037 | 0.394 | 6.411 | 1.0 |
| std | 148.12 | 1.081 | 0.316 | 2.976 | 0.498 | 0.189 | 4.185 | 0.026 | 0.270 | 0.169 | 2.823 | 0.474 | 0.185 | 3.323 | 0.0 |
| min | 71.00 | -5.284 | 0.004 | 2.250 | -1.497 | 0.018 | 2.000 | 0.750 | -1.960 | 0.000 | 2.500 | -1.499 | 0.044 | 2.000 | 1.0 |
| 25% | 218.00 | -0.407 | 0.271 | 4.250 | -0.342 | 0.224 | 4.875 | 1.000 | -0.149 | 0.060 | 4.000 | -0.279 | 0.256 | 4.429 | 1.0 |
| 50% | 343.00 | -0.025 | 0.424 | 5.500 | 0.069 | 0.337 | 6.500 | 1.000 | -0.014 | 0.133 | 5.500 | 0.090 | 0.364 | 5.714 | 1.0 |
| 75% | 469.00 | 0.190 | 0.642 | 7.000 | 0.328 | 0.481 | 8.750 | 1.000 | 0.115 | 0.242 | 7.000 | 0.324 | 0.501 | 7.429 | 1.0 |
| max | 602.00 | 2.335 | 1.989 | 39.750 | 0.640 | 1.000 | 64.380 | 1.000 | 2.008 | 1.795 | 40.000 | 0.660 | 0.998 | 49.140 | 1.0 |
Make nice interactive plot for correlation between binding and entry for EFNB2 and EFNB3¶
In [9]:
def plot_corr_binding_entry_updated(df,flag):
variant_selector = alt.selection_point(
on="mouseover",
empty=False,
fields=["site","mutant"],
value=0
)
variant_selector_agg = alt.selection_point(
on="mouseover",
empty=False,
fields=["site"],
value=0
)
slider = alt.binding_range(min=2, max=10, step=1, name="times seen")
selector = alt.param(name="SelectorName", value=2, bind=slider)
empty_chart = []
for cell in list(df['selection'].unique()):
tmp_df = df[df['selection'] == cell]
if flag == True:
agg_df = tmp_df.groupby('site')[['binding_median','effect']].sum().reset_index()
chart = alt.Chart(agg_df).mark_point(stroke='black',filled=True).encode(
x=alt.X('effect', title=f'Summed {cell} Cell Entry', axis=alt.Axis(grid=True)),
y=alt.Y('binding_median', title=f'Summed {cell} Binding', axis=alt.Axis(grid=True)),
opacity=alt.condition(variant_selector_agg, alt.value(1), alt.value(0.2)),
size=alt.condition(variant_selector_agg,alt.value(100),alt.value(50)),
strokeWidth=alt.condition(variant_selector_agg,alt.value(1),alt.value(0)),
color=alt.condition(variant_selector_agg,alt.value('orange'),alt.value('black')),
tooltip=['site', 'binding_median','effect'],
).add_params(variant_selector_agg)
empty_chart.append(chart)
else:
chart = alt.Chart(tmp_df).mark_point(stroke='black',filled=True).encode(
x=alt.X('effect', title=f'{cell} Cell Entry', axis=alt.Axis(grid=True)),
y=alt.Y('binding_median', title=f'{cell} Binding', axis=alt.Axis(grid=True)),
opacity=alt.condition(variant_selector, alt.value(1), alt.value(0.1)),
size=alt.condition(variant_selector,alt.value(50),alt.value(20)),
strokeWidth=alt.condition(variant_selector,alt.value(1),alt.value(0)),
color=alt.condition(variant_selector,alt.value('orange'),alt.value('black')),
tooltip=['site', 'wildtype', 'mutant','binding_median','times_seen_binding','effect'],
).add_params(variant_selector)
empty_chart.append(chart)
combined_chart = alt.hconcat(*empty_chart,title=alt.Title('Correlation between binding and entry'))
return combined_chart
entry_binding_corr_plot = plot_corr_binding_entry_updated(df_binding_effect_concat,False)
entry_binding_corr_plot.display()
if entry_binding_combined_corr_plot is not None:
entry_binding_corr_plot.save(entry_binding_combined_corr_plot)
entry_binding_corr_plot_agg = plot_corr_binding_entry_updated(df_binding_effect_concat,True)
entry_binding_corr_plot_agg.display()
if entry_binding_combined_corr_plot is not None:
entry_binding_corr_plot_agg.save(entry_binding_combined_corr_plot_agg)
In [10]:
def plot_entry_binding_corr_heatmap(df):
empty_chart = []
for cell in list(df['selection'].unique()):
tmp_df = df[df['selection'] == cell]
chart = alt.Chart(tmp_df,title=f'{cell}').mark_rect().encode(
x=alt.X('effect',title='Cell Entry',axis=alt.Axis(values=[-2,-1,0,1])).bin(maxbins=60),
y=alt.Y('binding_median',title='Binding',axis=alt.Axis(values=[-4,-2,0,2])).bin(maxbins=60),
color=alt.Color('count()',title='Count').scale(scheme='greenblue'),
)
empty_chart.append(chart)
combined_chart = alt.hconcat(*empty_chart,title=alt.Title('Correlation between binding and entry')).resolve_scale(y='shared',x='shared',color='shared')
return combined_chart
entry_binding_corr_heat = plot_entry_binding_corr_heatmap(df_binding_effect_concat)
entry_binding_corr_heat.display()
if entry_binding_combined_corr_plot is not None:
entry_binding_corr_heat.save(entry_binding_corr_heatmap)
In [11]:
def overall_stats(df,effect,name):
#Find quantiles
quantile_2 = df['binding_median'].quantile(.02)
quantile_98 = df['binding_median'].quantile(.98)
print(f'The 2% quantile for {name} is: {quantile_2}')
print(f'The 98% quantile for {name} is: {quantile_98}')
#Now group sites and find intolerant sites
filtered_df = df.groupby('site').filter(lambda group: (group[effect] <-0.25).all())
unique = filtered_df['site'].unique()
# Convert unique to a Pandas Series
unique_series = pd.Series(unique)
# Find the common elements
unique_contact_bool = unique_series.isin(config['contact_sites'])
# Filter and get the common elements
common_elements = unique_series[unique_contact_bool]
# Print the common elements
print(f'Here are the contact sites that are conserved: {common_elements}')
print(f'There are {len(unique)} sites with all negative binding score mutants for {name}')
print(f'These are the sites for {name} with all negative binding score mutants: {list(unique)}')
#Now find sites with low and high binding (median)
median_df = df.groupby('site')['binding_median'].median().reset_index().sort_values(by='binding_median')
print(f'For {name}, these are the sites with lowest median binding scores: {median_df.head(5)}')
median_df = df.groupby('site')['binding_median'].median().reset_index().sort_values(by='binding_median',ascending=False)
print(f'For {name}, these are the sites with highest median binding scores: {median_df.head(5)}')
#Now calculate mutant number
total_mutants = df.shape[0]
upper_cutoff = df[df[effect] > 1].sort_values(by='binding_median',ascending=False)
median_upper = upper_cutoff['effect'].median()
print(f'The median entry score for top binders was: {median_upper}')
mutants_above_cutoff_tolerated = upper_cutoff[upper_cutoff['effect'] > 0]
mutants_above_cutoff_tolerated = mutants_above_cutoff_tolerated[['site','effect','binding_median','wildtype','mutant']]
print(f'The mutants with positive entry scores and good binding are: {mutants_above_cutoff_tolerated.head(5)}')
lower_cutoff = df[df[effect] < -1]
print(f'For {name}, there are a total of : {total_mutants} binding mutants')
print(f'For {name}, there are {upper_cutoff.shape[0]} mutants above cutoff, and {mutants_above_cutoff_tolerated.shape[0]} that have good entry scores')
print(f'For {name}, there are {lower_cutoff.shape[0]} mutants below cutoff')
total_sites = df['site'].unique().shape[0]
print(f'The total number of sites are: {total_sites}')
overall_stats(df_E2_filter,'binding_median','E2')
overall_stats(df_E3_filter,'binding_median','E3')
The 2% quantile for E2 is: -3.875 The 98% quantile for E2 is: 1.112 Here are the contact sites that are conserved: 4 238 5 242 11 389 23 488 24 490 25 491 29 501 30 504 31 505 33 531 34 532 35 533 36 557 37 579 38 581 41 588 dtype: int64 There are 44 sites with all negative binding score mutants for E2 These are the sites for E2 with all negative binding score mutants: [116, 206, 220, 236, 238, 242, 243, 248, 346, 351, 352, 389, 390, 398, 399, 400, 438, 439, 441, 460, 467, 486, 487, 488, 490, 491, 494, 495, 497, 501, 504, 505, 526, 531, 532, 533, 557, 579, 581, 584, 585, 588, 590, 594] For E2, these are the sites with lowest median binding scores: site binding_median 442 533 -4.0450 406 494 -4.0250 403 490 -3.9855 401 487 -3.8680 413 504 -3.8420 For E2, these are the sites with highest median binding scores: site binding_median 132 208 1.3875 48 120 1.3630 59 132 1.2410 131 207 1.2080 249 331 1.1210 The median entry score for top binders was: -0.7595000000000001 The mutants with positive entry scores and good binding are: site effect binding_median wildtype mutant 1324 139 0.005 1.989 N Y 5448 354 0.228 1.498 S T 9533 566 0.081 1.415 F H 7170 444 0.163 1.256 I F 1217 134 0.099 1.249 S I For E2, there are a total of : 6601 binding mutants For E2, there are 176 mutants above cutoff, and 22 that have good entry scores For E2, there are 964 mutants below cutoff The total number of sites are: 510 The 2% quantile for E3 is: -0.63186 The 98% quantile for E3 is: 0.6008599999999996 Here are the contact sites that are conserved: 3 389 5 488 9 501 10 531 11 532 dtype: int64 There are 12 sites with all negative binding score mutants for E3 These are the sites for E3 with all negative binding score mutants: [108, 140, 352, 389, 486, 488, 494, 495, 497, 501, 531, 532] For E3, these are the sites with lowest median binding scores: site binding_median 413 501 -0.9160 439 531 -0.7235 307 389 -0.6870 404 488 -0.6440 440 532 -0.6310 For E3, these are the sites with highest median binding scores: site binding_median 492 589 0.6710 84 159 0.5610 56 129 0.5330 59 132 0.4675 86 161 0.4475 The median entry score for top binders was: -0.757 The mutants with positive entry scores and good binding are: site effect binding_median wildtype mutant 7995 492 0.496 1.200 Q L 2632 211 0.037 1.149 G F 10015 598 0.344 1.141 P G 1655 161 0.234 1.011 S E For E3, there are a total of : 6408 binding mutants For E3, there are 25 mutants above cutoff, and 4 that have good entry scores For E3, there are 11 mutants below cutoff The total number of sites are: 506
Find sites with opposite effects on binding¶
In [12]:
#find sites that are different
def find_biggest_differences(df):
efnb2_good_efnb3_bad = df[
(df['binding_median_E2'] > 0.1) &
(df['binding_median_E3'] < -0.1)
].sort_values(by='binding_median_E2',ascending=False)
display(efnb2_good_efnb3_bad)
efnb2_bad_efnb3_good = df[
(df['binding_median_E2'] < -0.1) &
(df['binding_median_E3'] > 0.1)
].sort_values(by='binding_median_E3',ascending=False)
display(efnb2_bad_efnb3_good)
find_biggest_differences(df_binding_effect_merge)
| site | wildtype | mutant | binding_median_E2 | binding_std_E2 | times_seen_binding_E2 | effect_E2 | effect_std_E2 | times_seen_cell_entry_E2 | frac_models_E2 | binding_median_E3 | binding_std_E3 | times_seen_binding_E3 | effect_E3 | effect_std_E3 | times_seen_cell_entry_E3 | frac_models_E3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 455 | 120 | I | M | 1.363 | 0.837 | 5.50 | -0.638 | 0.447 | 6.750 | 1.0 | -0.357 | 0.806 | 4.0 | -0.197 | 0.222 | 5.714 | 1.0 |
| 868 | 170 | E | T | 1.312 | 0.886 | 4.25 | -0.314 | 0.668 | 4.250 | 1.0 | -0.136 | 0.235 | 4.0 | -0.697 | 0.573 | 5.000 | 1.0 |
| 2524 | 303 | P | C | 1.251 | 1.588 | 5.00 | -0.921 | 0.678 | 5.000 | 1.0 | -0.542 | 0.057 | 4.0 | -0.664 | 0.585 | 4.857 | 1.0 |
| 6472 | 591 | K | G | 1.228 | 0.113 | 5.25 | -0.130 | 0.456 | 6.000 | 1.0 | -0.157 | 0.212 | 5.5 | 0.132 | 0.392 | 5.857 | 1.0 |
| 973 | 177 | G | M | 1.160 | 0.599 | 6.00 | 0.184 | 0.465 | 4.500 | 1.0 | -0.234 | 0.050 | 4.0 | -0.014 | 0.363 | 6.429 | 1.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4614 | 446 | D | F | 0.104 | 0.201 | 5.25 | 0.361 | 0.280 | 7.125 | 1.0 | -0.204 | 0.102 | 5.0 | 0.511 | 0.238 | 5.286 | 1.0 |
| 1600 | 223 | A | C | 0.103 | 0.426 | 4.50 | 0.226 | 0.252 | 7.250 | 1.0 | -0.145 | 0.129 | 5.0 | -0.063 | 0.337 | 5.429 | 1.0 |
| 4668 | 449 | G | P | 0.101 | 0.134 | 3.50 | -0.342 | 0.453 | 4.500 | 1.0 | -0.329 | 0.368 | 2.5 | 0.140 | 0.616 | 3.571 | 1.0 |
| 6510 | 596 | K | G | 0.101 | 0.808 | 6.00 | 0.309 | 0.284 | 5.750 | 1.0 | -0.277 | 0.027 | 4.5 | 0.390 | 0.270 | 6.143 | 1.0 |
| 6089 | 562 | I | E | 0.101 | 0.279 | 2.75 | -0.222 | 0.478 | 5.500 | 1.0 | -0.339 | 0.506 | 3.0 | 0.307 | 0.441 | 4.000 | 1.0 |
344 rows × 17 columns
| site | wildtype | mutant | binding_median_E2 | binding_std_E2 | times_seen_binding_E2 | effect_E2 | effect_std_E2 | times_seen_cell_entry_E2 | frac_models_E2 | binding_median_E3 | binding_std_E3 | times_seen_binding_E3 | effect_E3 | effect_std_E3 | times_seen_cell_entry_E3 | frac_models_E3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5217 | 492 | Q | L | -0.167 | 0.523 | 5.00 | 0.514 | 0.344 | 4.500 | 1.0 | 1.200 | 0.031 | 5.0 | 0.496 | 0.296 | 4.857 | 1.0 |
| 6532 | 598 | P | G | -0.200 | 0.772 | 4.50 | -1.051 | 0.970 | 4.500 | 1.0 | 1.141 | 1.564 | 4.0 | 0.344 | 0.542 | 5.000 | 1.0 |
| 6434 | 588 | I | P | -2.239 | 0.805 | 4.75 | -0.187 | 0.393 | 4.500 | 1.0 | 1.052 | 0.055 | 5.0 | -0.637 | 0.369 | 5.000 | 1.0 |
| 461 | 123 | N | G | -0.162 | 0.646 | 3.25 | -1.133 | 0.779 | 4.500 | 1.0 | 0.820 | 0.160 | 2.5 | 0.141 | 0.616 | 4.429 | 1.0 |
| 559 | 137 | S | D | -0.232 | 0.854 | 4.00 | -1.228 | 0.323 | 6.875 | 1.0 | 0.788 | 0.827 | 4.0 | -0.278 | 0.433 | 3.571 | 1.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 5368 | 507 | V | M | -1.357 | 0.772 | 6.00 | 0.419 | 0.080 | 7.375 | 1.0 | 0.102 | 0.614 | 4.0 | -0.441 | 0.506 | 5.143 | 1.0 |
| 2141 | 275 | N | V | -1.259 | 0.628 | 3.75 | -0.264 | 0.370 | 6.750 | 1.0 | 0.101 | 0.378 | 4.0 | -0.121 | 0.246 | 4.143 | 1.0 |
| 6136 | 567 | L | F | -0.313 | 0.242 | 12.25 | 0.335 | 0.264 | 15.500 | 1.0 | 0.101 | 0.152 | 12.5 | 0.102 | 0.302 | 15.570 | 1.0 |
| 5497 | 518 | N | E | -0.151 | 0.285 | 5.75 | 0.295 | 0.370 | 7.500 | 1.0 | 0.101 | 0.200 | 6.5 | 0.225 | 0.386 | 5.286 | 1.0 |
| 2842 | 324 | K | S | -0.203 | 0.732 | 4.00 | -0.328 | 0.680 | 5.500 | 1.0 | 0.101 | 0.024 | 4.0 | 0.217 | 0.197 | 4.571 | 1.0 |
202 rows × 17 columns
Find correlations between EFNB2 and EFNB3 binding¶
In [13]:
def plot_entry_binding_corr(df):
chart = alt.Chart(df,title='Correlation Between Mutant Binding Scores').mark_rect().encode(
x=alt.X('binding_median_E2',title='EFNB2 binding',axis=alt.Axis(values=[-5,0,2])).bin(maxbins=40),
y=alt.Y('binding_median_E3',title='EFNB3 binding',axis=alt.Axis(values=[-2,0,2])).bin(maxbins=40),
color=alt.Color('count()',title='Count').scale(scheme='greenblue'),
)
return chart
entry_binding_corr_heatmap_1 = plot_entry_binding_corr(df_binding_effect_merge)
entry_binding_corr_heatmap_1.display()
if entry_binding_combined_corr_plot is not None:
entry_binding_corr_heatmap_1.save(binding_corr_heatmap)
In [14]:
def plot_affinity_solid(df):
df = df.dropna()
# calculate r value
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(df['binding_median_E2'], df['binding_median_E3'])
r_value = float(r_value)
# make chart
chart = alt.Chart(df,title=alt.Title('Correlation between Mutant Binding Scores',subtitle=f'r={r_value:.2f}')).mark_point(color='black',size=30, opacity=0.2,filled=True).encode(
x=alt.X('binding_median_E2', title=('EFNB2 Binding')),
y=alt.Y('binding_median_E3', title=('EFNB3 Binding')),
tooltip=['site', 'wildtype','mutant','binding_median_E2','binding_median_E3','effect_E2','effect_E3'],
)
min = int(df['binding_median_E2'].min())
max = int(df['binding_median_E3'].max())
text = alt.Chart({'values':[{'x': min, 'y': max, 'text': f'r = {r_value:.2f}'}]}).mark_text(
align='left', baseline='top', dx=-10, dy=-20).encode(
x=alt.X('x:Q'),
y=alt.Y('y:Q'),
text='text:N'
)
chart_and_text = chart
return chart_and_text
E2_E3_corr = plot_affinity_solid(df_binding_effect_merge)
E2_E3_corr.display()
if entry_binding_combined_corr_plot is not None:
E2_E3_corr.save(E2_E3_correlation)
Plot correlations between summary statistics for each site¶
In [15]:
def plot_affinity_solid_mean(df):
df = df.dropna()
means = df.groupby('site').agg({
'effect_E2': 'median',
'effect_E3': 'median',
'binding_median_E2': 'median',
'binding_median_E3': 'median',
'wildtype': 'first'
}).reset_index()
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(means['binding_median_E2'], means['binding_median_E3'])
r_value = float(r_value)
chart = alt.Chart(means,title=alt.Title('Correlation between Aggregate Mutant Binding Scores',subtitle=f'r={r_value:.2f}')).mark_point(size=50, opacity=0.3).encode(
x=alt.X('binding_median_E2', title=('Median EFNB2 Binding'), axis=alt.Axis(tickCount=3)),
y=alt.Y('binding_median_E3', title=('Median EFNB3 Binding'), axis=alt.Axis(tickCount=3)),
tooltip=['site', 'wildtype','binding_median_E2','binding_median_E3','effect_E2','effect_E3'],
)
text = alt.Chart({'values':[{'x': -3.5, 'y': 0.5, 'text': f'r = {r_value:.2f}'}]}).mark_text(
align='left', baseline='top', dx=0, dy=-10).encode(
x=alt.X('x:Q'),
y=alt.Y('y:Q'),
text='text:N'
)
chart_and_text = chart
return chart_and_text
E2_E3_site_corr = plot_affinity_solid_mean(df_binding_effect_merge)
E2_E3_site_corr.display()
if entry_binding_combined_corr_plot is not None:
E2_E3_site_corr.save(E2_E3_correlation_site)
if entry_binding_combined_corr_plot is not None:
(E2_E3_site_corr | E2_E3_corr).save(combined_E2_E3_site_corr)
Make plot showing binding by site (median)¶
In [16]:
def plot_affinity_by_site_median(df):
variant_selector = alt.selection_point(
on="mouseover",
nearest=True,
empty=False,
fields=["site"],
value=0
)
empty_charts = []
for selection in ['binding_median_E2','binding_median_E3']:
if selection == 'binding_median_E2':
name = 'EFNB2 Binding'
else:
name = 'EFNB3 Binding'
mean = df.groupby('site')[selection].max().reset_index()
mean = mean[mean[selection] >= 0]
chart = alt.Chart(mean).mark_point(stroke='black',filled=True,size=50).encode(
x=alt.X('site', title=('Site'), axis=alt.Axis(grid=True, tickCount=4),scale=alt.Scale(domain=[70,602])),
y=alt.Y(selection, title=(name), axis=alt.Axis(grid=True, tickCount=3)),
tooltip=['site'],
color=alt.condition(variant_selector, alt.value('orange'), alt.value('black')),
opacity=alt.condition(variant_selector, alt.value(1), alt.value(0.5)),
strokeWidth=alt.condition(variant_selector,alt.value(1),alt.value(0))
).properties(height=150,width=500).add_params(variant_selector)
empty_charts.append(chart)
combined_chart = alt.vconcat(*empty_charts, spacing=1,title='Max Binding by Site')
return combined_chart
binding_by_site = plot_affinity_by_site_median(df_binding_effect_merge)
binding_by_site.display()
if entry_binding_combined_corr_plot is not None:
binding_by_site.save(binding_by_site_plot)
In [17]:
def plot_affinity_by_contact_site(df,sites_to_show,title_text):
variant_selector = alt.selection_point(
on="mouseover",
nearest=True,
empty=False,
fields=["site"],
value=0
)
empty_charts = []
contact_df = df[df['site'].isin(sites_to_show)]
sites = list(contact_df['site'].unique())
for selection in df['selection'].unique():
tmp_df = contact_df[contact_df['selection'] == selection]
mean = tmp_df.groupby('site')['binding_median'].max().reset_index()
chart = alt.Chart(mean).mark_point(size=100).encode(
x=alt.X('site:O', sort=sites,title=('Site'), axis=alt.Axis(grid=True, labelAngle=-90),scale=alt.Scale(domain=sites)),
y=alt.Y('binding_median', title=(f'{selection}'), axis=alt.Axis(grid=True)),
tooltip=['site'],
color=alt.condition(variant_selector, alt.value('orange'), alt.value('black')),
strokeWidth=alt.condition(variant_selector,alt.value(2),alt.value(0))
).add_params(variant_selector)
empty_charts.append(chart)
combined_chart = alt.vconcat(*empty_charts, spacing=1,title=title_text)
return combined_chart
contact_binding_by_site = plot_affinity_by_contact_site(df_binding_effect_concat,config['contact_sites'],'Max Binding in Contact')
contact_binding_by_site.display()
if entry_binding_combined_corr_plot is not None:
contact_binding_by_site.save(max_binding_in_contact)
contact_binding_by_site_stalk = plot_affinity_by_contact_site(df_binding_effect_concat,list(range(96, 147)),"Max Binding in Stalk")
contact_binding_by_site_stalk.display()
if entry_binding_combined_corr_plot is not None:
contact_binding_by_site_stalk.save(max_binding_in_stalk)
Make bubble plots for binding in different areas of receptor pocket¶
In [18]:
def make_boxplot_binding_region(df,title):# Create a box plot using Altair for aggregated means
barrel_ranges = {
'Hydrophobic': config['hydrophobic'],
'Salt Bridges': config['salt_bridges'],
'Hydrogen Bonds': config['h_bond_total'],
'Contact': config['contact_sites'],
'Overall': list(range(71,602)),
}
mean_df = df.groupby('site')[['binding_median']].median().reset_index()
custom_order = ['Hydrophobic','Salt Bridges','Hydrogen Bonds','Contact','Overall']
agg_means = []
# For each barrel, filter the site_means dataframe to the sites belonging to that barrel and then store the means
for barrel, sites in barrel_ranges.items():
subset = mean_df[mean_df['site'].isin(sites)]
for _, row in subset.iterrows():
agg_means.append({'barrel': barrel, 'effect': row['binding_median'],'site':row['site']})
agg_means_df = pd.DataFrame(agg_means)
chart = alt.Chart(agg_means_df).mark_point(size=50,opacity=0.4).encode(
x=alt.X('barrel:O', sort=custom_order,title=None,axis=alt.Axis(labelAngle=-90)),
y=alt.Y('effect',title=f'Median {title} Binding',axis=alt.Axis(grid=True,tickCount=4)),
xOffset='random:Q',
tooltip=['barrel', 'effect','site'],
).transform_calculate(
random="sqrt(-1*log(random()))*cos(2*PI*random())"
)
return chart.display()
make_boxplot_binding_region(df_E2_filter,'EFNB2')
make_boxplot_binding_region(df_E3_filter,'EFNB3')
make boxplot of binding scores by region¶
In [19]:
def make_boxplot_binding_region(df):
barrel_ranges = {
"Stalk": list(range(96, 147)),
"Neck": list(range(148, 165)),
"Linker": list(range(166, 177)),
"Head": list(range(178, 602)),
'Receptor Contact': config['contact_sites'],
"Total": list(range(71, 602)),
}
custom_order = ["Stalk", "Neck", "Linker", "Head", "Receptor Contact", "Total"]
empty_charts = []
for selection in df['selection'].unique():
tmp_df = df[df["selection"] == selection]
agg_means = []
# For each barrel, filter the site_means dataframe to the sites belonging to that barrel and then store the means
for barrel, sites in barrel_ranges.items():
subset = tmp_df[tmp_df["site"].isin(sites)]
for _, row in subset.iterrows():
agg_means.append(
{"region": barrel, "binding_median": row["binding_median"], "site": row["site"]}
)
agg_means_df = pd.DataFrame(agg_means)
chart = (
alt.Chart(agg_means_df, title=f"{selection}")
.mark_boxplot(color="darkgray", extent="min-max", opacity=1)
.encode(
x=alt.X(
"region:O",
sort=custom_order,
title="RBP Region",
axis=alt.Axis(labelAngle=-90),
),
y=alt.Y(
"binding_median",
title=f"Binding",
axis=alt.Axis(grid=True, tickCount=4),
),
tooltip=["region", "binding_median", "site"],
).properties(width=config['width'],height=config['height'])
)
empty_charts.append(chart)
combined_effect_chart = alt.hconcat(*empty_charts).resolve_scale(
y="shared", x="shared", color="independent"
)
return combined_effect_chart
entry_region_boxplot = make_boxplot_binding_region(df_binding_effect_concat)
entry_region_boxplot.display()
if entry_binding_combined_corr_plot is not None:
entry_region_boxplot.save(binding_region_boxplot_plot)
In [20]:
def make_bubble_binding_region(df):
barrel_ranges = {
"Stalk": list(range(96, 147)),
"Neck": list(range(148, 165)),
"Linker": list(range(166, 177)),
"Head": list(range(178, 602)),
'Receptor Contact': config['contact_sites'],
"Total": list(range(71, 602)),
}
custom_order = ["Stalk", "Neck", "Linker", "Head", "Receptor Contact", "Total"]
empty_charts = []
for selection in df['selection'].unique():
tmp_df = df[df["selection"] == selection]
agg_means = []
# For each barrel, filter the site_means dataframe to the sites belonging to that barrel and then store the means
for barrel, sites in barrel_ranges.items():
subset = tmp_df[tmp_df["site"].isin(sites)]
for _, row in subset.iterrows():
agg_means.append(
{"region": barrel, "binding_median": row["binding_median"], "site": row["site"],"mutant": row["mutant"]}
)
agg_means_df = pd.DataFrame(agg_means)
variant_selector = alt.selection_point(
on="mouseover", empty=False, fields=["site",'mutant'], value=1
)
chart = (
alt.Chart(agg_means_df, title=f"{selection}")
.mark_point(opacity=0.3, stroke='black')
.encode(
x=alt.X(
"region:O",
sort=custom_order,
title="RBP Region",
axis=alt.Axis(labelAngle=-90),
),
y=alt.Y(
"binding_median",
title=f"Binding",
axis=alt.Axis(grid=True, tickCount=4),
),
xOffset="random:Q",
tooltip=["region", "binding_median", "site","mutant"],
color=alt.condition(
variant_selector, alt.value("orange"), alt.value("black")
),
opacity=alt.condition(variant_selector, alt.value(1), alt.value(0.1)),
strokeWidth=alt.condition(variant_selector,alt.value(2),alt.value(0)),
size=alt.condition(variant_selector,alt.value(50),alt.value(15)),
).transform_calculate(
random="sqrt(-1*log(random()))*cos(2*PI*random())"
).properties(width=config['width'],height=config['height'])
).add_params(variant_selector)
empty_charts.append(chart)
combined_effect_chart = alt.hconcat(*empty_charts).resolve_scale(
y="shared", x="shared", color="independent"
).add_params(variant_selector)
return combined_effect_chart
entry_region_bubble = make_bubble_binding_region(df_binding_effect_concat)
entry_region_bubble.display()
if entry_binding_combined_corr_plot is not None:
entry_region_bubble.save(binding_region_bubble_plot)
In [ ]: